Aggregate Drought Index Research Articles

Application of meteorological, hydrological and remote sensing data to develop a hybrid index for drought assessment

ABSTRACT In this study, a hybrid index called the aggregated drought index (ADI) is developed for drought assessment integrating various meteorological, hydrological, and agricultural features of the region. To this end, precipitation, reservoir storage, discharge, temperature, potential evapotranspiration, and the degree of vegetation based on remote sensing images are utilized to quantify ADI. Then, the performance of two models, auto-regressive integrated moving average (ARIMA) and adaptive neuro-fuzzy inference system (ANFIS), is investigated in predicting the developed drought index. The proposed framework is applied to the Aharchay watershed located in East Azarbaijan Province, Iran. The results demonstrate that the region has experienced normal and mild drought conditions during the investigated time period. ADI is also applied in another watershed (Ajichay) to show ADI’s capability in different climatic settings. Regarding the predictive capability, the ANFIS model outperforms ARIMA in drought prediction, particularly in severe weather conditions. The ADI benefits planning for drought mitigation and preparedness by incorporating several different aspects of the region.

Comparison of Long-Term Changes in Non-Linear Aggregated Drought Index Calibrated by MERRA–2 and NDII Soil Moisture Proxies

This study aimed at evaluating Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA–2) and Normalized Difference Infrared Index (NDII) soil moisture proxies in calibrating a comprehensive Non-linear Aggregated Drought Index (NADI). Soil moisture plays a critical role in temperature variability and controlling the partitioning of water into evaporative fluxes as well as ensuring effective plant growth. Long-term variability and change in climatic variables such as precipitation, temperatures, and the possible acceleration of the water cycle increase the uncertainty in soil moisture variability. Streamflow, temperature, rainfall, reservoir storage, MERRA–2, and NDII soil moisture proxies’ data from 1986 to 2016 were used to formulate the NADI. The trend analysis was performed using the Mann Kendall, SQ-MK was used to determine the point of trend direction change while Theil-Sen trend estimator method was used to determine the magnitude of the detected trend. The seasonal correlation between the NADI-NDII and NADI-MERRA–2 was higher in spring and autumn with an R2 of 0.9 and 0.86, respectively. A positive trend was observed over the 30 years period of study, NADI-NDII trend magnitude was found to be 0.02 units per year while that of NADI-MERRA–2 was 0.01 units. Wavelet analysis showed an in-phase relationship with negligible lagging between the NDII and MERRA–2 calibrated NADI. Although a robust comparison is recommended between soil moisture proxies and observed soil moisture, the soil moisture proxies in this study were found to be useful in monitoring long-term changes in soil moisture.

Drought analyses of Eastern Mediterranean, Seyhan, Ceyhan, and Asi Basins by using aggregate drought index (ADI)

Drought analyses of Eastern Mediterranean, Seyhan, Ceyhan, and Asi Basins by using aggregate drought index (ADI)

The Impact of Climate Change on Hydro-Meteorological Droughts Using Copula Functions

Climate change has made many alterations to the climate of earth, including hydro-climatic extreme events. To investigate the impact of climate change on hydro-meteorological droughts in the Kamal-Saleh dam basin in Markazi province, Iran, proportional to future climate conditions, a new and comprehensive index was developed with the aim of accurately estimating drought in a more realistic condition. This aggregate drought index (ADI) represented the main meteorological and hydrological characteristics of drought. Temperature and precipitation projections for future climates were simulated by five CMIP5 models and downscaled over the study area during 2050s (2040–2069) and 2080s (2070–2099) relative to the baseline period (1976–2005). By fitting five univariate distribution functions on drought severity and duration, proper marginal distributions were selected. The joint distribution of drought severity and duration was chosen from five types of copula functions. The results revealed that in future, severe droughts are expected to frequently occur in a shorter period.

Hydroclimatic aggregate drought index (HADI): a new approach for identification and categorization of drought in cold climate regions

Drought identification is crucial to water resources management and planning. Different drought indices have been developed and their complexity and applicability vary. The objectives of this research are to develop a new integrated drought index with the capability of identification of drought and to further customize drought categorization for cold climate regions. Specifically, a new hydroclimatic aggregate drought index (HADI) is developed by coupling with a grid-based hydrologic model and applying the R-mode correlation-based principal component analysis. The HADI is a composite drought index, which assesses the anomalies of rainfall, surface runoff, snowmelt, and soil moisture in the root zone. Furthermore, joint probability distribution function of drought frequencies and classes as well as conditional expectation are used for drought categorization. The HADI was applied to the Red River of the North Basin (RRB) and its performance was evaluated by comparing with the Palmer Drought Severity Index (PDSI) and the U.S. Drought Monitor (USDM) products. Based on the impacts of drought on agriculture, the HADI outperformed the PDSI in identification of droughts in the RRB. Although the HADI and USDM showed a good agreement in identification of drought periods, the drought area coverages for each drought category from the two methods differed. The new customized drought categorization based on variable threshold levels accounted for the variations in both time and geographical locations. The new HADI, together with the customized drought categorization, is able to provide more accurate drought identification and characterization, especially for cold climate regions.

On the mechanisms of two composite methods for construction of multivariate drought indices

Droughts are comprehensive and complex issues that need to be characterized from a multivariate perspective. In recent years, a number of composite indices have been proposed for drought characterization. However, rare studies have systematically compared similarities and dissimilarities of these indices, and they have provided little insights into the combination mechanisms. To address this issue, two widely used combination approaches, namely the principal component analysis (PCA) and copula based joint probability distribution were employed, with the corresponding integrated product denoted as the Aggregate Drought Index (ADI) and Joint Drought Deficit Index (JDI). Five constituents for constructing ADI and JDI were derived from the variable infiltration capacity model (VIC) monthly simulations over the Yellow River basin (YRB), China, including precipitation (P), actual evapotranspiration (ET), soil moisture of top two layers, and runoff (during 1961–2012). Results showed that the behavioral patterns of ADI and JDI may not be easily influenced by the variation of one single element, and they represented comprehensive moisture status well. A further comparison between these two composite indices suggested that ADI and JDI behaved similarly in most areas of YRB, with some dissimilarities in the source region. The particular behavior of ET was responsible for the inconsistency. Comparing to other regions, an enhanced role of potential evapotranspiration (PET) was imposed on ET in the source region, leading to a poor relationship of ET with P and other hydrological variables. Accordingly, when constructing composite drought indices, the drought information indicated by ET was more easily abandoned by ADI but reserved in JDI. This study clearly demonstrates the mechanisms of two common integrated approaches in blending different drought information, which has significant implications for composite drought indices construction and application, and potentially provides some valuable references for the improvement of monitoring techniques in future drought related researches.

Comparison of the Performance of Six Drought Indices in Characterizing Historical Drought for the Upper Blue Nile Basin, Ethiopia

The Upper Blue Nile (UBN) basin is less-explored in terms of drought studies as compared to other parts of Ethiopia and lacks a basin-specific drought monitoring system. This study compares six drought indices: Standardized Precipitation Index (SPI), Standardized Precipitation Evaporation Index (SPEI), Evapotranspiration Deficit Index (ETDI), Soil Moisture Deficit Index (SMDI), Aggregate Drought Index (ADI), and Standardized Runoff-discharge Index (SRI), and evaluates their performance with respect to identifying historic drought events in the UBN basin. The indices were calculated using monthly time series of observed precipitation, average temperature, river discharge, and modeled evapotranspiration and soil moisture from 1970 to 2010. The Pearson’s correlation coefficients between the six drought indices were analyzed. SPI and SPEI at 3-month aggregate period showed high correlation with ETDI and SMDI (r > 0.62), while SPI and SPEI at 12-month aggregate period correlate better with SRI. The performance of the six drought indices in identifying historic droughts: 1973–1974, 1983–1984, 1994–1995, and 2003–2004 was analyzed using data obtained from Emergency Events Database (EM-DAT) and previous studies. When drought onset dates indicated by the six drought indices are compared with that in the EM-DAT. SPI, and SPEI showed early onsets of drought events, except 2003–2004 drought for which the onset date was unavailable in EM-DAT. Similarly, ETDI, SMDI and SRI-3 showed early onset for two drought events and late onsets in one-drought event. In contrast, ADI showed late onsets for two drought events and early onset for one drought event. None of the six drought indices could individually identify the onsets of all the selected historic drought events; however, they may identify the onsets when combined by considering several input variables at different aggregate periods.

Assessment of droughts and wheat yield loss on the North China Plain with an aggregate drought index (ADI) approach

Frequent drought events are threatening regional/global food security. However, current models for predicting crop yield loss suffered from drought threat is still limited. In this study, seven drought indices were employed to develop an aggregate drought index (ADI) to quantify the drought effect on crop yield in the North China Plain (NCP). Based on time-series and panel regression models, relationships between climatic yield anomalies of winter wheat (ΔY) and ADI values before sowing of winter wheat and during its growth were investigated. Results showed that ADI assembled the advantages of drought indices and reasonably described the range, severity and duration of drought events. During the study period, trends of annual ADI displayed remarkable spatial heterogeneity in the growth stages. Increasing trends of ADI over 0.005/year were found in most areas of NCP at STAGE 1 (from July to September in last year). At STAGE 4 (from grain-filling to maturation), increasing and decreasing trends were observed in northern and southern NCP, respectively. Based on panel model, ADI in the four stages could predict about 20% variations of ΔY in areas with high irrigation supplies. Meanwhile, more than 40% variations of ΔY may be predicted in eastern NCP with low irrigation supplies. The study highlights the importance of integrating drought information and irrigation for assessment of winter wheat yield variations.

Hydro-Meteorological Drought Assessment Based on Aggregate Drought Index (ADI) and its prediction with Markov Chain in Sarbaz River Basin (Southeast of Iran)

Hydro-Meteorological Drought Assessment Based on Aggregate Drought Index (ADI) and its prediction with Markov Chain in Sarbaz River Basin (Southeast of Iran)

Development of a new composite drought index for multivariate drought assessment

Summary Comprehensibly considering all physical forms of agricultural, hydrological, and meteorological drought is essential to develop reliable monitoring and prediction indices for the proper assessment of drought. This consideration encouraged to develop and evaluate a multivariate composite drought index (CDI) that considers all possible variables related to individual types of drought. The proposed CDI was primarily based on the weighted similarity measure (entropy weighted Euclidian distance) and the anomaly from the possible wettest and driest conditions of the selected study region (sub basin of Han River, South Korea). The CDI time series identified 2008–2009 as the driest year, while May 2008 was the driest month within the selected period (2003–2011). The comparative analysis revealed that the CDI monthly time series had a significant correlation with the aggregate drought index (ADI). In addition, in comparison with the single variable-based indices i.e., the standardized precipitation index (SPI) and the streamflow drought index (SDI), the CDI comprehensively responded to variability embedded in the individual drought attributes. Moreover, it was concluded that the developed CDI provided a physically sound, temporally flexible and unbiased index that can be directly associated with all possible variants and linked to the climate conditions of the study region without considering any feature extraction technique.

Historical extension of operational NDVI products for livestock insurance in Kenya

Droughts induce livestock losses that severely affect Kenyan pastoralists. Recent index insurance schemes have the potential of being a viable tool for insuring pastoralists against drought-related risk. Such schemes require as input a forage scarcity (or drought) index that can be reliably updated in near real-time, and that strongly relates to livestock mortality. Generally, a long record (>25 years) of the index is needed to correctly estimate mortality risk and calculate the related insurance premium. Data from current operational satellites used for large-scale vegetation monitoring span over a maximum of 15 years, a time period that is considered insufficient for accurate premium computation. This study examines how operational NDVI datasets compare to, and could be combined with the non-operational recently constructed 30-year GIMMS AVHRR record (1981–2011) to provide a near-real time drought index with a long term archive for the arid lands of Kenya. We compared six freely available, near-real time NDVI products: five from MODIS and one from SPOT-VEGETATION. Prior to comparison, all datasets were averaged in time for the two vegetative seasons in Kenya, and aggregated spatially at the administrative division level at which the insurance is offered. The feasibility of extending the resulting aggregated drought indices back in time was assessed using jackknifed R2 statistics (leave-one-year-out) for the overlapping period 2002–2011. We found that division-specific models were more effective than a global model for linking the division-level temporal variability of the index between NDVI products. Based on our results, good scope exists for historically extending the aggregated drought index, thus providing a longer operational record for insurance purposes. We showed that this extension may have large effects on the calculated insurance premium. Finally, we discuss several possible improvements to the drought index.

English

Drought is a climatic anomaly, characterized by shortage (lack) of rainfall, high evaporation and unsuitable distribution of rainfall. This study investigated and compared the aggregated drought index (ADI) and standardized precipitation index (SPI) for drought monitoring in Minab watershed in Hormozgan province in south of Iran. Precipitation data were used for SPI calculation. Four variables including precipitation, potential evaporation, temperature and peak discharge (4 available recorded data) were used for ADI calculations using principal component analysis (PCA). Results of SPI show that watershed has normal drought situation from 1980 to 2009. On the other hand, ADI shows capability to detect dry and wet years. Moreover, it has capability to detect historical drought. Overall, by considering the results, the ADI is more reliable than SPI for drought monitoring in the study area. Key words: Drought, standardized precipitation index (SPI), aggregated drought index (ADI), Brentin, Minab, Hormozgan, Iran.

Artificial Neural Network–Based Drought Forecasting Using a Nonlinear Aggregated Drought Index

AbstractDrought forecasting plays an important role in the planning and management of water resources systems, especially during dry climatic periods. In this study, a nonlinear aggregated drought index (NADI) was developed first to classify the drought condition of a catchment considering all significant hydrometeorological variables that have effects on droughts. An artificial neural network (ANN)—based drought forecasting approach was then developed by using the time series of the NADI to forecast NADI values. In forecasting future drought conditions, the NADI produces the overall dryness within the system as compared to the traditional forecasting of rainfall deficiency, which considers only the meteorological droughts. Two ANN forecasting models, namely a recursive multistep neural network (RMSNN) and a direct multistep neural network (DMSNN), were developed in this study. Overall, these models were capable of forecasting drought conditions well for up to 6 months of future forecasts, which were stat...

Drought Assessment and Forecasting: A Case Study on the Yarra River Catchment in Victoria, Australia

Drought is a natural phenomenon, and has widespread and significant impacts on the world’s economy, environment, industries and the wider community. Early detection of droughts helps to implement drought mitigation strategies and measures before they occur. Therefore, drought forecasting plays an important role in the planning and management of water resources systems, especially during dry climatic periods. However, drought assessment and forecasting are not always easy tasks. In this paper, an approach that was developed and tested for drought assessment and forecasting is presented together with a case study on the Yarra River catchment in Victoria (Australia). An evaluation of existing drought indices was first conducted in this study, which led to the development of a new non-linear aggregated drought index (NADI). The NADI defines a broad perspective of dryness within the catchment rather than the traditional individual meteorological, hydrological and agricultural subcategories. Thereafter, a novel drought forecasting modelling approach was developed using the NADI time series. The results show that the developed forecasting models are capable of forecasting drought conditions well up to 6 months ahead forecasts which were statistically significant at 1% level. The outcomes of this study will be useful for water resources managers to assess droughts effectively and forecast future drought conditions, which will allow them to plan ahead the future water management activities especially during drought periods.

Drought forecasting using an aggregated drought index and artificial neural network

Early forecasting of future drought conditions during continuing dry periods can improve water resources management strategies. In this study, a drought forecasting approach is developed and presented using an aggregated drought index (ADI) and artificial neural network (ANN) using a monthly time step. The use of ADI forecasts the overall availability of water resources beyond the traditional forecasting of rainfall deficiency to represent future drought conditions. The paper compares two types of ANN; namely, recursive multi-step neural networks (RMSNN) and direct multi-step neural networks (DMSNN). The results show that the RMSNN approach is slightly better than the DMSNN approach for forecasts with lead time up to 3 months. The DMSNN approach gives slightly better results than the RMSNN approach when forecast lead time is over 3 months, and can give reasonable results up to 6 months ahead of forecasts.

Comparative Evaluation of Drought Indexes: Case Study on the Yarra River Catchment in Australia

Drought Indexes (DIs) have commonly been used to quantify drought conditions around the world. Most DIs are developed for specific regions and therefore may not be directly applicable to other regions due to inherent complexity of drought phenomena, different hydro-climatic conditions and different catchment characteristics. There had been few studies cited in the literature where suitability of some of the existing DIs was evaluated for different parts of the world. However, no such study was conducted in Australia where droughts are more frequent than elsewhere in the world. In this paper, five DIs namely Percent of Normal (PN), Deciles, Standardized Precipitation Index (SPI), Surface Water Supply Index (SWSI) and Aggregated Drought Index (ADI) were evaluated for the Yarra River catchment in Victoria (Australia). Evaluation was conducted based on how well these indices modeled the historical droughts. Five decision criteria, namely robustness, tractability, sophistication, transparency, and extendability were also used to compare the performance of these DIs. The results showed that the ADI was superior to other indices for drought management within the Yarra River catchment.

Optimal Drought Management Using Sampling Stochastic Dynamic Programming with a Hedging Rule

This study develops procedures that calculate optimal water release curtailments during droughts using a future value function derived with a sampling stochastic dynamic programming model. Triggers that switch between a normal operating policy and an emergency operating policy (EOP) are based on initial reservoir storage values representing a 95% water supply reliability and an aggregate drought index that employs 6-month cumulative rainfall and 4-month cumulative streamflow. To verify the effectiveness of the method, a cross-validation scheme (using 2,100 combination sets) is employed to simulate the Geum River basin system in Korea. The simulation results demonstrate that the EOP approach: (1) reduces the maximum water shortage; (2) is most valuable when the initial storages of the drawdown period are low; and (3) is superior to other approaches when explicitly considering forecast uncertainty.

An aggregate drought index: Assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage

An aggregate drought index (ADI) has been developed, and evaluated within three diverse climate divisions in California. The ADI comprehensively considers all physical forms of drought (meteorological, hydrological, and agricultural) through selection of variables that are related to each drought type. Water stored in large surface water reservoirs was also included. Hydroclimatic monthly data for each climate division underwent correlation‐based principal component analysis (PCA), and the first principal component was deseasonalized to arrive at a single ADI value for each month. ADI time series were compared against the Palmer Drought Severity Index (PDSI) to describe two important droughts in California, the 1976–1977 and 1987–1992 events, from a hydroclimatological perspective. The ADI methodology provides a clear, objective approach for describing the intensity of drought and can be readily adapted to characterize drought on an operational basis.